When shopping online, users usually use product rankings as a reference to determine which products to buy. Product rankings which are currently available to the users include rankings based on prices, geographical locations, credibility ratings, volume of sales, etc. These rankings, however, may not allow the users to find products they want to buy because whether a product is suitable for a user is not merely a matter of prices or credit ratings. Although credit ratings reflect qualities of products to some extent, a product having a high credit rating cannot be guaranteed to be the product that is desired by a user. Besides factors such as prices and credit ratings, user interests and hobbies are also factors that determine whether a certain product is favorably selected by a user. Existing product ranking methods only take factors like prices and credit ratings into account, without considering factors such as user interests and hobbies, thus failing to provide a ranking method that incorporates user characteristics. As such, users cannot quickly obtain their expected search results and need to perform searching for multiple times with relatively long searching times. Furthermore, the burden on network communication and the processing load of servers are increased, reducing the processing performance of the servers.
Furthermore, degrees of familiarity between users and merchants may also affect decision of the users in selecting products. If a user or his/her friend has previously conducted a successful transaction with a merchant, this merchant will have a higher likelihood of being selected by the user as compared to merchants who have not developed any relationship with the user. Existing online shopping platforms only rate creditability of a merchant based on transaction records. However, if a user has very different shopping habits or interests and hobbies than users who have previously conducted successful transactions with the merchant, the user may still not favorably select this merchant who has a good credit rating. Therefore, the existing methods that are based on past transaction records of merchants fail to incorporate user characteristics in ratings and ranking. Existing rating and ranking methods fail to incorporate user characteristics for processing, thus leading users fail to quickly obtain expected search results. The users need to perform searching for multiple times with relatively long searching times. Furthermore, the burden of network communication and the processing load of servers are increased, thus reducing the processing performance of the servers.